US20100220893A1 - Method and System of Mono-View Depth Estimation - Google Patents
Method and System of Mono-View Depth Estimation Download PDFInfo
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- US20100220893A1 US20100220893A1 US12/396,363 US39636309A US2010220893A1 US 20100220893 A1 US20100220893 A1 US 20100220893A1 US 39636309 A US39636309 A US 39636309A US 2010220893 A1 US2010220893 A1 US 2010220893A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30221—Sports video; Sports image
Definitions
- the present invention generally relates to mono-view depth estimation, and more particularly to a ground model for mono-view depth estimation.
- 3D depth information When three-dimensional (3D) objects are mapped onto a two-dimensional (2D) image plane by prospective projection, such as with an image taken by a still camera or video captured by a video camera, a substantial amount of information, such as the 3D depth information, disappears because of the non-unique many-to-one transformation. Accordingly, an image point cannot uniquely determine its depth. Recapture or generation of the 3D depth information is thus a challenging task that is crucial in recovering a full, or at least an approximate, 3D representation.
- depth may be obtained from the monoscopic spatial and/or temporal domain.
- the term “monoscopic” or “mono” is used herein to refer to a characteristic in which the left and right eyes see the same perspective view of a given scene.
- One of the known mono-view depth estimation methods is performed by extracting the depth information from the degree of object motion, and is thus called a depth-from-motion method. The object with a higher degree of motion is assigned smaller (or nearer) depth, and vice versa.
- Another one of the conventional mono-view depth estimation methods is performed by assigning larger (or farther) depth to non-focused regions such as the background, and is thus called a depth-from-focus-cue method.
- a further conventional mono-view depth estimation methods is performed by detecting the intersection of vanishing lines, or vanishing point. The points approaching the vanishing point are assigned larger (or farther) depth, and vice versa.
- DDR depth diffusion region
- a two-dimensional (2D) image is first segmented into a number of objects.
- a DDR such as for example the ground or a floor, is then detected among the objects.
- the DDR generally includes a region or relatively planar region that is about horizontal (e.g., a horizontal plane).
- the DDR is assigned a depth, such as for example, a depth monotonically increasing from bottom to top of the DDR.
- An object connected to the DDR is assigned depth according to the depth of the DDR at the connected location. For example, the depth of the connected object is assigned the same depth of the DDR at the connected location.
- FIG. 1 illustrates a flow diagram demonstrating the steps of a mono-view depth estimation method based on a ground model according to one embodiment of the present invention
- FIG. 3 shows an exemplary image, in which a golfer stands on the ground or other surface capable of serving as a depth diffusion region (DDR).
- DDR depth diffusion region
- FIG. 1 illustrates a flow diagram demonstrating the steps of a mono-view depth estimation method 100 based on a ground model according to one embodiment of the present invention.
- FIG. 2 illustrates an associated block diagram of a mono-view depth estimation system 200 according to the embodiment of the present invention.
- an input device 20 provides or receives one or more two-dimensional (2D) input images to be image/video processed in accordance with the embodiment of the present invention.
- the input device 20 may in general be an electro-optical device that maps 3D object(s) onto a 2D image plane by prospective projection.
- the input device 20 may be a still camera that takes the 2D image, or a video camera that captures a number of image frames.
- the input device 20 in another embodiment, may be a pre-processing device that performs one or more of digital image processing tasks, such as image enhancement, image restoration, image analysis, image compression or image synthesis.
- the input device 20 may further include a storage device, such as a semiconductor memory or hard disk drive, which stores processed images from the pre-processing device.
- a storage device such as a semiconductor memory or hard disk drive, which stores processed images from the pre-processing device.
- a relatively large amount of information such as particularly the 3D depth information, is lost when 3D objects are mapped onto the 2D image plane, and therefore according to a feature of the invention the 2D image provided by the input device 20 is subjected to image/video processing through other blocks of the mono-view depth estimation system 200 , which will be discussed below.
- the input image/video is then processed, in step 12 , by a segmentation unit 22 that partitions the input image into multiple regions, objects or segments.
- the term “unit” is used to denote a circuit, a piece of program, or their combination.
- the method and system of the present invention may be implemented in whole or in part using software and/or firmware, including, for example, one or more of a computer, a microprocessor, a circuit, an Application Specific Integrated Circuit (ASIC), a programmable gate array device, or other hardware.
- the purpose of the segmentation is to change the representation of the image into something easier to assign depth to in the later steps.
- the pixels in the same region have similar characteristics, such as color, intensity or texture, while the pixels between adjacent regions have distinct characteristics.
- Step 12 may be performed using one of the conventional segmentation techniques, or may be performed using a segmentation technique to be developed in the future.
- a depth diffusion region is detected by a DDR detection unit 24 .
- the DDR may be ground (or earth), ocean, flooring or any other region or surface that is about horizontal (e.g., a horizontal plane).
- a horizontal plane having the same segmentation characteristics and having substantive area can, according to a feature of the invention, probably be detected as the DDR.
- FIG. 3 shows an exemplary image in which a golfer 30 stands on the ground (or the lawn) 32 or other region (e.g., horizontal plane or relatively horizontal surface) suitable for serving as the DDR.
- two objects i.e., the ground 32 and the golfer 30
- the DDR is assigned depth in step 15 by a DDR depth assignment unit 26 .
- the depth assignment of the DDR (for example, the ground 32 ) may monotonically increase from the bottom to the top.
- the depth magnitude of the DDR can be inversely proportional to a vertical dimension of the DDR or location on the DDR.
- the depth assignment of the DDR may be formulated as follows:
- the depth of the object (or objects) connected to the DDR is assigned by the depth assignment unit 26 , according to the DDR depth at the connected site. Taking the image in FIG. 3 as an example, as the golfer 30 is connected to (or standing on) the DDR at the bottom of his or her feet, the depth of the golfer 30 is assigned the same depth of the DDR 32 at the connected site; that is, y Obj .
- the depth assignment may be formulated as follows:
- DepthObj Depth DDR ( y Obj )
- the image or partial image is assigned depth according to one of the conventional assignment methods or a technique to be developed in the future.
- the foreground(s) and background(s) of the non-DDR image are detected (in step 16 ), followed by assigning corresponding depth to the foregrounds/backgrounds (in step 17 ) according to the conventional method.
- the foreground is assigned depth values smaller than those of the background.
- the depth obtained from step 15 alone or together with the depth obtained from step 17 are combined (in step 18 ) to arrive at a final depth map.
- An output device 28 receives the depth map information (e.g., the final depth map) from the DDR depth assignment unit 26 and provides a resulting or output image.
- the output device 28 may be a display device for presentation or viewing of the received depth information (e.g., depth map information).
- the output device 28 in another embodiment, may be a storage device, such as a semiconductor memory or hard disk drive, which stores the received depth information.
- the output device 28 may further and/or alternatively include a post-processing device that performs one or more of digital image processing tasks, such as image enhancement, image restoration, image analysis, image compression or image synthesis.
- the ground model methods and systems for mono-view depth estimation are capable of providing correct and versatile depth and handling of a relatively large variety of scenes whenever a DDR is present or capable of being determined or estimated.
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Abstract
Description
- 1. Field of the Invention
- The present invention generally relates to mono-view depth estimation, and more particularly to a ground model for mono-view depth estimation.
- 2. Description of the Prior Art
- When three-dimensional (3D) objects are mapped onto a two-dimensional (2D) image plane by prospective projection, such as with an image taken by a still camera or video captured by a video camera, a substantial amount of information, such as the 3D depth information, disappears because of the non-unique many-to-one transformation. Accordingly, an image point cannot uniquely determine its depth. Recapture or generation of the 3D depth information is thus a challenging task that is crucial in recovering a full, or at least an approximate, 3D representation.
- In mono-view depth estimation, depth may be obtained from the monoscopic spatial and/or temporal domain. The term “monoscopic” or “mono” is used herein to refer to a characteristic in which the left and right eyes see the same perspective view of a given scene. One of the known mono-view depth estimation methods is performed by extracting the depth information from the degree of object motion, and is thus called a depth-from-motion method. The object with a higher degree of motion is assigned smaller (or nearer) depth, and vice versa. Another one of the conventional mono-view depth estimation methods is performed by assigning larger (or farther) depth to non-focused regions such as the background, and is thus called a depth-from-focus-cue method. A further conventional mono-view depth estimation methods is performed by detecting the intersection of vanishing lines, or vanishing point. The points approaching the vanishing point are assigned larger (or farther) depth, and vice versa.
- As very limited information may be obtained from the monoscopic spatio-temporal domain, the conventional methods mentioned above, unfortunately, cannot solve all of the scene-contents in a real-world video/image. For the foregoing reason, a need has arisen to propose a novel depth estimation method generally for a versatile mono-view video/image.
- In view of the foregoing, it is an object of the present invention to provide a ground model method and system for mono-view depth estimation, which is capable of providing correct and versatile depth and handling of a relatively large (i.e., great) variety of scenes whenever a depth diffusion region (DDR) is present or can be identified.
- According to one embodiment, a two-dimensional (2D) image is first segmented into a number of objects. A DDR, such as for example the ground or a floor, is then detected among the objects. The DDR generally includes a region or relatively planar region that is about horizontal (e.g., a horizontal plane). The DDR is assigned a depth, such as for example, a depth monotonically increasing from bottom to top of the DDR. An object connected to the DDR is assigned depth according to the depth of the DDR at the connected location. For example, the depth of the connected object is assigned the same depth of the DDR at the connected location.
-
FIG. 1 illustrates a flow diagram demonstrating the steps of a mono-view depth estimation method based on a ground model according to one embodiment of the present invention; -
FIG. 2 illustrates an associated block diagram of a mono-view depth estimation system according to the embodiment of the present invention; and -
FIG. 3 shows an exemplary image, in which a golfer stands on the ground or other surface capable of serving as a depth diffusion region (DDR). -
FIG. 1 illustrates a flow diagram demonstrating the steps of a mono-viewdepth estimation method 100 based on a ground model according to one embodiment of the present invention.FIG. 2 illustrates an associated block diagram of a mono-viewdepth estimation system 200 according to the embodiment of the present invention. - In
step 11, aninput device 20 provides or receives one or more two-dimensional (2D) input images to be image/video processed in accordance with the embodiment of the present invention. Theinput device 20 may in general be an electro-optical device that maps 3D object(s) onto a 2D image plane by prospective projection. In one embodiment, theinput device 20 may be a still camera that takes the 2D image, or a video camera that captures a number of image frames. Theinput device 20, in another embodiment, may be a pre-processing device that performs one or more of digital image processing tasks, such as image enhancement, image restoration, image analysis, image compression or image synthesis. Moreover, theinput device 20 may further include a storage device, such as a semiconductor memory or hard disk drive, which stores processed images from the pre-processing device. As discussed above, a relatively large amount of information, such as particularly the 3D depth information, is lost when 3D objects are mapped onto the 2D image plane, and therefore according to a feature of the invention the 2D image provided by theinput device 20 is subjected to image/video processing through other blocks of the mono-viewdepth estimation system 200, which will be discussed below. - The input image/video is then processed, in
step 12, by asegmentation unit 22 that partitions the input image into multiple regions, objects or segments. As used herein, the term “unit” is used to denote a circuit, a piece of program, or their combination. In general, the method and system of the present invention may be implemented in whole or in part using software and/or firmware, including, for example, one or more of a computer, a microprocessor, a circuit, an Application Specific Integrated Circuit (ASIC), a programmable gate array device, or other hardware. The purpose of the segmentation is to change the representation of the image into something easier to assign depth to in the later steps. The pixels in the same region have similar characteristics, such as color, intensity or texture, while the pixels between adjacent regions have distinct characteristics.Step 12 may be performed using one of the conventional segmentation techniques, or may be performed using a segmentation technique to be developed in the future. - In
step 13, a depth diffusion region (DDR) is detected by aDDR detection unit 24. According to the disclosed ground model of the present embodiment, the DDR may be ground (or earth), ocean, flooring or any other region or surface that is about horizontal (e.g., a horizontal plane). A horizontal plane having the same segmentation characteristics and having substantive area can, according to a feature of the invention, probably be detected as the DDR.FIG. 3 shows an exemplary image in which agolfer 30 stands on the ground (or the lawn) 32 or other region (e.g., horizontal plane or relatively horizontal surface) suitable for serving as the DDR. In this exemplary image, two objects (i.e., theground 32 and the golfer 30) are collected through the segmentation of theprevious step 12. - When a DDR is identified (i.e., the yes branch of step 14), the DDR is assigned depth in
step 15 by a DDRdepth assignment unit 26. The depth assignment of the DDR (for example, the ground 32) may monotonically increase from the bottom to the top. According to one feature of the invention, the depth magnitude of the DDR can be inversely proportional to a vertical dimension of the DDR or location on the DDR. The depth assignment of the DDR may be formulated as follows: -
DepthDDR(y) ↑ as y ↓ - or
-
DepthDDR=k/y - where k is a constant.
- In another embodiment, depth assignment of the DDR may increase from the bottom to the top in a non-monotonic manner. For example, DepthDDR=k/(y2).
- Further, the depth of the object (or objects) connected to the DDR is assigned by the
depth assignment unit 26, according to the DDR depth at the connected site. Taking the image inFIG. 3 as an example, as thegolfer 30 is connected to (or standing on) the DDR at the bottom of his or her feet, the depth of thegolfer 30 is assigned the same depth of the DDR 32 at the connected site; that is, yObj. The depth assignment may be formulated as follows: -
DepthObj=DepthDDR(y Obj) - Generally speaking, when a connected object rests or stands on the DDR (or the ground) at a connected point, the depth of the whole object is then assigned the same depth of the DDR at the connected or joined point.
- When no DDR is identified or the object(s) are not connected to the DDR (i.e., the no branch of step 14), the image or partial image is assigned depth according to one of the conventional assignment methods or a technique to be developed in the future. In the flow diagram of
FIG. 1 , the foreground(s) and background(s) of the non-DDR image are detected (in step 16), followed by assigning corresponding depth to the foregrounds/backgrounds (in step 17) according to the conventional method. In general, the foreground is assigned depth values smaller than those of the background. The depth obtained fromstep 15 alone or together with the depth obtained fromstep 17 are combined (in step 18) to arrive at a final depth map. - An
output device 28 receives the depth map information (e.g., the final depth map) from the DDRdepth assignment unit 26 and provides a resulting or output image. Theoutput device 28, in one embodiment, may be a display device for presentation or viewing of the received depth information (e.g., depth map information). Theoutput device 28, in another embodiment, may be a storage device, such as a semiconductor memory or hard disk drive, which stores the received depth information. Moreover, theoutput device 28 may further and/or alternatively include a post-processing device that performs one or more of digital image processing tasks, such as image enhancement, image restoration, image analysis, image compression or image synthesis. - According to the embodiment discussed above, the ground model methods and systems for mono-view depth estimation are capable of providing correct and versatile depth and handling of a relatively large variety of scenes whenever a DDR is present or capable of being determined or estimated.
- Although specific embodiments have been illustrated and described, it will be appreciated by those skilled in the art that various modifications may be made without departing from the scope of the present invention, which is intended to be limited solely by the appended claims.
Claims (16)
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Cited By (7)
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US20130321571A1 (en) * | 2011-02-23 | 2013-12-05 | Koninklijke Philips N.V. | Processing depth data of a three-dimensional scene |
US9324184B2 (en) | 2011-12-14 | 2016-04-26 | Microsoft Technology Licensing, Llc | Image three-dimensional (3D) modeling |
US9406153B2 (en) | 2011-12-14 | 2016-08-02 | Microsoft Technology Licensing, Llc | Point of interest (POI) data positioning in image |
US10008021B2 (en) | 2011-12-14 | 2018-06-26 | Microsoft Technology Licensing, Llc | Parallax compensation |
US10015478B1 (en) * | 2010-06-24 | 2018-07-03 | Steven M. Hoffberg | Two dimensional to three dimensional moving image converter |
US10038842B2 (en) | 2011-11-01 | 2018-07-31 | Microsoft Technology Licensing, Llc | Planar panorama imagery generation |
US10164776B1 (en) | 2013-03-14 | 2018-12-25 | goTenna Inc. | System and method for private and point-to-point communication between computing devices |
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US20090196492A1 (en) * | 2008-02-01 | 2009-08-06 | Samsung Electronics Co., Ltd. | Method, medium, and system generating depth map of video image |
US20100046837A1 (en) * | 2006-11-21 | 2010-02-25 | Koninklijke Philips Electronics N.V. | Generation of depth map for an image |
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US6031564A (en) * | 1997-07-07 | 2000-02-29 | Reveo, Inc. | Method and apparatus for monoscopic to stereoscopic image conversion |
US20100046837A1 (en) * | 2006-11-21 | 2010-02-25 | Koninklijke Philips Electronics N.V. | Generation of depth map for an image |
US20090196492A1 (en) * | 2008-02-01 | 2009-08-06 | Samsung Electronics Co., Ltd. | Method, medium, and system generating depth map of video image |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
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US10015478B1 (en) * | 2010-06-24 | 2018-07-03 | Steven M. Hoffberg | Two dimensional to three dimensional moving image converter |
US11470303B1 (en) | 2010-06-24 | 2022-10-11 | Steven M. Hoffberg | Two dimensional to three dimensional moving image converter |
US20130321571A1 (en) * | 2011-02-23 | 2013-12-05 | Koninklijke Philips N.V. | Processing depth data of a three-dimensional scene |
US9338424B2 (en) * | 2011-02-23 | 2016-05-10 | Koninklijlke Philips N.V. | Processing depth data of a three-dimensional scene |
US10038842B2 (en) | 2011-11-01 | 2018-07-31 | Microsoft Technology Licensing, Llc | Planar panorama imagery generation |
US9324184B2 (en) | 2011-12-14 | 2016-04-26 | Microsoft Technology Licensing, Llc | Image three-dimensional (3D) modeling |
US9406153B2 (en) | 2011-12-14 | 2016-08-02 | Microsoft Technology Licensing, Llc | Point of interest (POI) data positioning in image |
US10008021B2 (en) | 2011-12-14 | 2018-06-26 | Microsoft Technology Licensing, Llc | Parallax compensation |
US10164776B1 (en) | 2013-03-14 | 2018-12-25 | goTenna Inc. | System and method for private and point-to-point communication between computing devices |
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